Cargando…

Understanding the structure of cognitive noise

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhu, Jian-Qiao, León-Villagrá, Pablo, Chater, Nick, Sanborn, Adam N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423631/
https://www.ncbi.nlm.nih.gov/pubmed/35976980
http://dx.doi.org/10.1371/journal.pcbi.1010312
_version_ 1784778061846151168
author Zhu, Jian-Qiao
León-Villagrá, Pablo
Chater, Nick
Sanborn, Adam N.
author_facet Zhu, Jian-Qiao
León-Villagrá, Pablo
Chater, Nick
Sanborn, Adam N.
author_sort Zhu, Jian-Qiao
collection PubMed
description Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.
format Online
Article
Text
id pubmed-9423631
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-94236312022-08-30 Understanding the structure of cognitive noise Zhu, Jian-Qiao León-Villagrá, Pablo Chater, Nick Sanborn, Adam N. PLoS Comput Biol Research Article Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world. Public Library of Science 2022-08-17 /pmc/articles/PMC9423631/ /pubmed/35976980 http://dx.doi.org/10.1371/journal.pcbi.1010312 Text en © 2022 Zhu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhu, Jian-Qiao
León-Villagrá, Pablo
Chater, Nick
Sanborn, Adam N.
Understanding the structure of cognitive noise
title Understanding the structure of cognitive noise
title_full Understanding the structure of cognitive noise
title_fullStr Understanding the structure of cognitive noise
title_full_unstemmed Understanding the structure of cognitive noise
title_short Understanding the structure of cognitive noise
title_sort understanding the structure of cognitive noise
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423631/
https://www.ncbi.nlm.nih.gov/pubmed/35976980
http://dx.doi.org/10.1371/journal.pcbi.1010312
work_keys_str_mv AT zhujianqiao understandingthestructureofcognitivenoise
AT leonvillagrapablo understandingthestructureofcognitivenoise
AT chaternick understandingthestructureofcognitivenoise
AT sanbornadamn understandingthestructureofcognitivenoise